code: def test_given_some_time_series_are_too_short_then_seasonal_naive_forecast_is_used(
      temp_model_path,
      model_type,
      differences,
      eval_metric,
  ):
      data = get_data_frame_with_variable_lengths({"A": 50, "B": 60})
      prediction_length = 5
      model = model_type(
          path=temp_model_path,
          freq=data.freq,
          hyperparameters={"differences": differences},
          prediction_length=prediction_length,
          eval_metric=eval_metric,
      )
      model.fit(train_data=data)
  
      df_with_short = get_data_frame_with_variable_lengths(
          {
              "A": sum(differences),
              "B": sum(differences) + 5,
              "C": sum(differences) + 100,
          },
          freq=model.freq,
      )
      with mock.patch(
          "autogluon.timeseries.models.local.naive.SeasonalNaiveModel.predict"
      ) as snaive_predict:
          try:
              model.predict(df_with_short)
          except TypeError:
              pass
          assert snaive_predict.call_args[0][0].equals(
              df_with_short.loc[["A"]]
          )
  
file_path: autogluon/timeseries/tests/unittests/models/test_mlforecast.py
imports: from unittest import mock
  import numpy
  import pandas
  import pytest
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.models.autogluon_tabular.mlforecast import DirectTabularModel, RecursiveTabularModel
  from autogluon.timeseries.utils.features import TimeSeriesFeatureGenerator
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_ts_dataframe
  from ..common import DATAFRAME_WITH_COVARIATES, DATAFRAME_WITH_STATIC, DUMMY_VARIABLE_LENGTH_TS_DATAFRAME, get_data_frame_with_variable_lengths
label_desc: Use image node_id 7 for calling a global function with example usage: test_given_some_time_series_are_too_short_then_seasonal_naive_forecast_is_used(temp_model_path, model_type, differences, eval_metric) without return types